


import importlib.metadata
import warnings
from copy import deepcopy

from packaging import version

from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available


if is_bitsandbytes_available():
    import bitsandbytes as bnb
    import torch
    import torch.nn as nn

    from ..pytorch_utils import ConvaD

if is_accelerate_available():
    from accelerate import init_empty_weights
    from accelerate.utils import find_tied_parameters

__lowerCAmelCase     							=			logging.get_logger(__name__)
def 			__lowerCamelCase			( _lowerCAmelCase						,  _lowerCAmelCase						,  _lowerCAmelCase						,  _lowerCAmelCase=None						,  _lowerCAmelCase=None	)					->	Tuple:




  if "." in tensor_name:
    _UpperCAmelCase												=     tensor_name.split("."	)
    for split in splits[:-1]:
      _UpperCAmelCase												=     getattr(_lowerCAmelCase						,  _lowerCAmelCase	)
      if new_module is None:
        raise ValueError(F'''{module} has no attribute {split}.'''	)
      _UpperCAmelCase												=     new_module
    _UpperCAmelCase												=     splits[-1]

  if tensor_name not in module._parameters and tensor_name not in module._buffers:
    raise ValueError(F'''{module} does not have a parameter or a buffer named {tensor_name}.'''	)
  _UpperCAmelCase												=     tensor_name in module._buffers
  _UpperCAmelCase												=     getattr(_lowerCAmelCase						,  _lowerCAmelCase	)

  if old_value.device == torch.device("meta"	) and device not in ["meta", torch.device("meta"	)] and value is None:
    raise ValueError(F'''{tensor_name} is on the meta device, we need a `value` to put in on {device}.'''	)

  _UpperCAmelCase												=     False
  _UpperCAmelCase												=     False
  if is_buffer or not is_bitsandbytes_available():
    _UpperCAmelCase												=     False
    _UpperCAmelCase												=     False
  else:
    _UpperCAmelCase												=     hasattr(bnb.nn						,  "Params4bit"	) and isinstance(module._parameters[tensor_name]						,  bnb.nn.Paramsabit	)
    _UpperCAmelCase												=     isinstance(module._parameters[tensor_name]						,  bnb.nn.IntaParams	)

  if is_abit or is_abit:
    _UpperCAmelCase												=     module._parameters[tensor_name]
    if param.device.type != "cuda":
      if value is None:
        _UpperCAmelCase												=     old_value.to(_lowerCAmelCase	)
      elif isinstance(_lowerCAmelCase						,  torch.Tensor	):
        _UpperCAmelCase												=     value.to("cpu"	)
        if value.dtype == torch.inta:
          _UpperCAmelCase												=     version.parse(importlib.metadata.version("bitsandbytes"	)	) > version.parse(
              "0.37.2"	)
          if not is_abit_serializable:
            raise ValueError(
                "Detected int8 weights but the version of bitsandbytes is not compatible with int8 serialization. "
                "Make sure to download the latest `bitsandbytes` version. `pip install --upgrade bitsandbytes`."	)
      else:
        _UpperCAmelCase												=     torch.tensor(_lowerCAmelCase						,  device="cpu"	)

      # Support models using `Conv1D` in place of `nn.Linear` (e.g. gpt2) by transposing the weight matrix prior to quantization.
      # Since weights are saved in the correct "orientation", we skip transposing when loading.
      if issubclass(module.source_cls						,  _lowerCAmelCase	) and fpaa_statistics is None:
        _UpperCAmelCase												=     new_value.T

      _UpperCAmelCase												=     old_value.__dict__
      if is_abit:
        _UpperCAmelCase												=     bnb.nn.IntaParams(_lowerCAmelCase						,  requires_grad=_lowerCAmelCase						,  **_lowerCAmelCase	).to(_lowerCAmelCase	)
      elif is_abit:
        _UpperCAmelCase												=     bnb.nn.Paramsabit(_lowerCAmelCase						,  requires_grad=_lowerCAmelCase						,  **_lowerCAmelCase	).to(_lowerCAmelCase	)

      _UpperCAmelCase												=     new_value
      if fpaa_statistics is not None:
        setattr(module.weight						,  "SCB"						,  fpaa_statistics.to(_lowerCAmelCase	)	)

  else:
    if value is None:
      _UpperCAmelCase												=     old_value.to(_lowerCAmelCase	)
    elif isinstance(_lowerCAmelCase						,  torch.Tensor	):
      _UpperCAmelCase												=     value.to(_lowerCAmelCase	)
    else:
      _UpperCAmelCase												=     torch.tensor(_lowerCAmelCase						,  device=_lowerCAmelCase	)

    if is_buffer:
      _UpperCAmelCase												=     new_value
    else:
      _UpperCAmelCase												=     nn.Parameter(_lowerCAmelCase						,  requires_grad=old_value.requires_grad	)
      _UpperCAmelCase												=     new_value
def 			__lowerCamelCase			( _lowerCAmelCase						,  _lowerCAmelCase=None						,  _lowerCAmelCase=None						,  _lowerCAmelCase=None						,  _lowerCAmelCase=False	)					->	List[str]:




  for name, module in model.named_children():
    if current_key_name is None:
      _UpperCAmelCase												=     []
    current_key_name.append(_lowerCAmelCase	)

    if (isinstance(_lowerCAmelCase						,  nn.Linear	) or isinstance(_lowerCAmelCase						,  _lowerCAmelCase	)) and name not in modules_to_not_convert:
      # Check if the current key is not in the `modules_to_not_convert`
      if not any(key in ".".join(_lowerCAmelCase	) for key in modules_to_not_convert	):
        with init_empty_weights():
          if isinstance(_lowerCAmelCase						,  _lowerCAmelCase	):
            _UpperCAmelCase						,		_UpperCAmelCase												=     module.weight.shape
          else:
            _UpperCAmelCase												=     module.in_features
            _UpperCAmelCase												=     module.out_features

          if quantization_config.quantization_method() == "llm_int8":
            _UpperCAmelCase												=     bnb.nn.LinearabitLt(
                _lowerCAmelCase						,  _lowerCAmelCase						,  module.bias is not None						,  has_fpaa_weights=quantization_config.llm_inta_has_fpaa_weight						,  threshold=quantization_config.llm_inta_threshold						,  )
            _UpperCAmelCase												=     True
          else:
            if (
                quantization_config.llm_inta_skip_modules is not None
                and name in quantization_config.llm_inta_skip_modules
            ):
              pass
            else:
              _UpperCAmelCase												=     bnb.nn.Linearabit(
                  _lowerCAmelCase						,  _lowerCAmelCase						,  module.bias is not None						,  quantization_config.bnb_abit_compute_dtype						,  compress_statistics=quantization_config.bnb_abit_use_double_quant						,  quant_type=quantization_config.bnb_abit_quant_type						,  )
              _UpperCAmelCase												=     True
                    # Store the module class in case we need to transpose the weight later
          _UpperCAmelCase												=     type(_lowerCAmelCase	)
          # Force requires grad to False to avoid unexpected errors
          model._modules[name].requires_grad_(_lowerCAmelCase	)
    if len(list(module.children()	)	) > 0:
      _UpperCAmelCase						,		_UpperCAmelCase												=     _replace_with_bnb_linear(
          _lowerCAmelCase						,  _lowerCAmelCase						,  _lowerCAmelCase						,  _lowerCAmelCase						,  has_been_replaced=_lowerCAmelCase						,  )
    # Remove the last key for recursion
    current_key_name.pop(-1	)
  return model, has_been_replaced
def 			__lowerCamelCase			( _lowerCAmelCase						,  _lowerCAmelCase=None						,  _lowerCAmelCase=None						,  _lowerCAmelCase=None	)					->	Optional[int]:
  _UpperCAmelCase												=     ["lm_head"] if modules_to_not_convert is None else modules_to_not_convert
  _UpperCAmelCase						,		_UpperCAmelCase												=     _replace_with_bnb_linear(
      _lowerCAmelCase						,  _lowerCAmelCase						,  _lowerCAmelCase						,  _lowerCAmelCase	)

  if not has_been_replaced:
    logger.warning(
        "You are loading your model in 8bit or 4bit but no linear modules were found in your model."
        " Please double check your model architecture, or submit an issue on github if you think this is"
        " a bug."	)

  return model
def 			__lowerCamelCase			( *_lowerCAmelCase						,  **_lowerCAmelCase	)					->	int:
  warnings.warn(
      "`replace_8bit_linear` will be deprecated in a future version, please use `replace_with_bnb_linear` instead"						,  _lowerCAmelCase						,  )
  return replace_with_bnb_linear(*_lowerCAmelCase						,  **_lowerCAmelCase	)
def 			__lowerCamelCase			( *_lowerCAmelCase						,  **_lowerCAmelCase	)					->	Any:
  warnings.warn(
      "`set_module_8bit_tensor_to_device` will be deprecated in a future version, please use `set_module_quantized_tensor_to_device` instead"						,  _lowerCAmelCase						,  )
  return set_module_quantized_tensor_to_device(*_lowerCAmelCase						,  **_lowerCAmelCase	)




def 			__lowerCamelCase			( _lowerCAmelCase	)					->	Union[str, Any]:
  _UpperCAmelCase												=     deepcopy(_lowerCAmelCase	)  # this has 0 cost since it is done inside `init_empty_weights` context manager`
  tied_model.tie_weights()

  _UpperCAmelCase												=     find_tied_parameters(_lowerCAmelCase	)
  # For compatibility with Accelerate < 0.18
  if isinstance(_lowerCAmelCase						,  _lowerCAmelCase	):
    _UpperCAmelCase												=     sum(list(tied_params.values()	)						,  []	) + list(tied_params.keys()	)
  else:
    _UpperCAmelCase												=     sum(_lowerCAmelCase						,  []	)
  _UpperCAmelCase												=     len(_lowerCAmelCase	) > 0

  # Check if it is a base model
  _UpperCAmelCase												=     not hasattr(_lowerCAmelCase						,  model.base_model_prefix	)

  # Ignore this for base models (BertModel, GPT2Model, etc.)
  if (not has_tied_params) and is_base_model:
    return []

  # otherwise they have an attached head
  _UpperCAmelCase												=     list(model.named_children()	)
  _UpperCAmelCase												=     [list_modules[-1][0]]

  # add last module together with tied weights
  _UpperCAmelCase												=     set(_lowerCAmelCase	) - set(_lowerCAmelCase	)
  _UpperCAmelCase												=     list(set(_lowerCAmelCase	)	) + list(_lowerCAmelCase	)

  # remove ".weight" from the keys
  _UpperCAmelCase												=     [".weight", ".bias"]
  _UpperCAmelCase												=     []
  for name in list_untouched:
    for name_to_remove in names_to_remove:
      if name_to_remove in name:
        _UpperCAmelCase												=     name.replace(_lowerCAmelCase						,  ""	)
    filtered_module_names.append(_lowerCAmelCase	)

  return filtered_module_names










"""simple docstring"""





import collections
import inspect
import unittest

from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch
       from torch import nn

       from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
       from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
       from PIL import Image

       from transformers import AutoImageProcessor

class 						lowercase   :





      def __init__(			self ,		lowercase ,		lowercase=13 ,		lowercase=32 ,		lowercase=2 ,		lowercase=3 ,		lowercase=16 ,		lowercase=[1, 2, 1] ,		lowercase=[2, 2, 4] ,		lowercase=2 ,		lowercase=2.0 ,		lowercase=True ,		lowercase=0.0 ,		lowercase=0.0 ,		lowercase=0.1 ,		lowercase="gelu" ,		lowercase=False ,		lowercase=True ,		lowercase=0.02 ,		lowercase=1e-5 ,		lowercase=True ,		lowercase=None ,		lowercase=True ,		lowercase=10 ,		lowercase=8 ,		)						->	Union[str, Any]:
            lowerCAmelCase    		=			parent
            lowerCAmelCase    		=			batch_size
            lowerCAmelCase    		=			image_size
            lowerCAmelCase    		=			patch_size
            lowerCAmelCase    		=			num_channels
            lowerCAmelCase    		=			embed_dim
            lowerCAmelCase    		=			depths
            lowerCAmelCase    		=			num_heads
            lowerCAmelCase    		=			window_size
            lowerCAmelCase    		=			mlp_ratio
            lowerCAmelCase    		=			qkv_bias
            lowerCAmelCase    		=			hidden_dropout_prob
            lowerCAmelCase    		=			attention_probs_dropout_prob
            lowerCAmelCase    		=			drop_path_rate
            lowerCAmelCase    		=			hidden_act
            lowerCAmelCase    		=			use_absolute_embeddings
            lowerCAmelCase    		=			patch_norm
            lowerCAmelCase    		=			layer_norm_eps
            lowerCAmelCase    		=			initializer_range
            lowerCAmelCase    		=			is_training
            lowerCAmelCase    		=			scope
            lowerCAmelCase    		=			use_labels
            lowerCAmelCase    		=			type_sequence_label_size
            lowerCAmelCase    		=			encoder_stride





      def        _snake_case     (			self					)						->	Tuple:
            lowerCAmelCase    		=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

            lowerCAmelCase    		=			None
            if self.use_labels:
                  lowerCAmelCase    		=			ids_tensor([self.batch_size] ,		self.type_sequence_label_size					)

            lowerCAmelCase    		=			self.get_config()

            return config, pixel_values, labels





      def        _snake_case     (			self					)						->	str:
            return SwinvaConfig(
                image_size=self.image_size ,		patch_size=self.patch_size ,		num_channels=self.num_channels ,		embed_dim=self.embed_dim ,		depths=self.depths ,		num_heads=self.num_heads ,		window_size=self.window_size ,		mlp_ratio=self.mlp_ratio ,		qkv_bias=self.qkv_bias ,		hidden_dropout_prob=self.hidden_dropout_prob ,		attention_probs_dropout_prob=self.attention_probs_dropout_prob ,		drop_path_rate=self.drop_path_rate ,		hidden_act=self.hidden_act ,		use_absolute_embeddings=self.use_absolute_embeddings ,		path_norm=self.patch_norm ,		layer_norm_eps=self.layer_norm_eps ,		initializer_range=self.initializer_range ,		encoder_stride=self.encoder_stride ,		)





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		lowercase					)						->	Tuple:
            lowerCAmelCase    		=			SwinvaModel(config=lowercase					)
            model.to(lowercase					)
            model.eval()
            lowerCAmelCase    		=			model(lowercase					)

            lowerCAmelCase    		=			((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths					) - 1))
            lowerCAmelCase    		=			int(config.embed_dim * 2 ** (len(config.depths					) - 1)					)

            self.parent.assertEqual(result.last_hidden_state.shape ,		(self.batch_size, expected_seq_len, expected_dim)					)





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		lowercase					)						->	str:
            lowerCAmelCase    		=			SwinvaForMaskedImageModeling(config=lowercase					)
            model.to(lowercase					)
            model.eval()
            lowerCAmelCase    		=			model(lowercase					)
            self.parent.assertEqual(
                result.logits.shape ,		(self.batch_size, self.num_channels, self.image_size, self.image_size)					)

            # test greyscale images
            lowerCAmelCase    		=			1
            lowerCAmelCase    		=			SwinvaForMaskedImageModeling(lowercase					)
            model.to(lowercase					)
            model.eval()

            lowerCAmelCase    		=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
            lowerCAmelCase    		=			model(lowercase					)
            self.parent.assertEqual(result.logits.shape ,		(self.batch_size, 1, self.image_size, self.image_size)					)





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		lowercase					)						->	Dict:
            lowerCAmelCase    		=			self.type_sequence_label_size
            lowerCAmelCase    		=			SwinvaForImageClassification(lowercase					)
            model.to(lowercase					)
            model.eval()
            lowerCAmelCase    		=			model(lowercase ,		labels=lowercase					)
            self.parent.assertEqual(result.logits.shape ,		(self.batch_size, self.type_sequence_label_size)					)





      def        _snake_case     (			self					)						->	Tuple:
            lowerCAmelCase    		=			self.prepare_config_and_inputs()
            lowerCAmelCase		,						lowerCAmelCase		,						lowerCAmelCase    		=			config_and_inputs
            lowerCAmelCase    		=			{"""pixel_values""": pixel_values}
            return config, inputs_dict







@require_torch
class 						lowercase   (   _UpperCAmelCase	,    _UpperCAmelCase	,    unittest.TestCase   ):
      _SCREAMING_SNAKE_CASE							      = (
          (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
      )
      _SCREAMING_SNAKE_CASE							      = (
          {'feature-extraction': SwinvaModel, 'image-classification': SwinvaForImageClassification}
          if is_torch_available()
          else {}
      )

      _SCREAMING_SNAKE_CASE							      = False
      _SCREAMING_SNAKE_CASE							      = False
      _SCREAMING_SNAKE_CASE							      = False
      _SCREAMING_SNAKE_CASE							      = False





      def        _snake_case     (			self					)						->	Any:
            lowerCAmelCase    		=			SwinvaModelTester(self					)
            lowerCAmelCase    		=			ConfigTester(self ,		config_class=lowercase ,		embed_dim=37					)





      def        _snake_case     (			self					)						->	Tuple:
            self.config_tester.create_and_test_config_to_json_string()
            self.config_tester.create_and_test_config_to_json_file()
            self.config_tester.create_and_test_config_from_and_save_pretrained()
            self.config_tester.create_and_test_config_with_num_labels()
            self.config_tester.check_config_can_be_init_without_params()
            self.config_tester.check_config_arguments_init()





      def        _snake_case     (			self					)						->	int:
            lowerCAmelCase    		=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*lowercase					)





      @unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0."""					)
      def        _snake_case     (			self					)						->	str:
            pass





      @unittest.skip(reason="""Swinv2 does not use inputs_embeds"""					)
      def        _snake_case     (			self					)						->	str:
            pass





      def        _snake_case     (			self					)						->	Tuple:
            lowerCAmelCase		,						lowerCAmelCase    		=			self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                  lowerCAmelCase    		=			model_class(lowercase					)
                  self.assertIsInstance(model.get_input_embeddings() ,		(nn.Module)					)
                  lowerCAmelCase    		=			model.get_output_embeddings()
                  self.assertTrue(x is None or isinstance(lowercase ,		nn.Linear					)					)





      def        _snake_case     (			self					)						->	Optional[int]:
            lowerCAmelCase		,						lowerCAmelCase    		=			self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                  lowerCAmelCase    		=			model_class(lowercase					)
                  lowerCAmelCase    		=			inspect.signature(model.forward					)
                  # signature.parameters is an OrderedDict => so arg_names order is deterministic
                  lowerCAmelCase    		=			[*signature.parameters.keys()]

                  lowerCAmelCase    		=			["""pixel_values"""]
                  self.assertListEqual(arg_names[:1] ,		lowercase					)





      def        _snake_case     (			self					)						->	Tuple:
            lowerCAmelCase		,						lowerCAmelCase    		=			self.model_tester.prepare_config_and_inputs_for_common()
            lowerCAmelCase    		=			True

            for model_class in self.all_model_classes:
                  lowerCAmelCase    		=			True
                  lowerCAmelCase    		=			False
                  lowerCAmelCase    		=			True
                  lowerCAmelCase    		=			model_class(lowercase					)
                  model.to(lowercase					)
                  model.eval()
                  with torch.no_grad():
                        lowerCAmelCase    		=			model(**self._prepare_for_class(lowercase ,		lowercase					)					)
                  lowerCAmelCase    		=			outputs.attentions
                  lowerCAmelCase    		=			len(self.model_tester.depths					)
                  self.assertEqual(len(lowercase					) ,		lowercase					)

                  # check that output_attentions also work using config
                  del inputs_dict["output_attentions"]
                  lowerCAmelCase    		=			True
                  lowerCAmelCase    		=			config.window_size**2
                  lowerCAmelCase    		=			model_class(lowercase					)
                  model.to(lowercase					)
                  model.eval()
                  with torch.no_grad():
                        lowerCAmelCase    		=			model(**self._prepare_for_class(lowercase ,		lowercase					)					)
                  lowerCAmelCase    		=			outputs.attentions
                  self.assertEqual(len(lowercase					) ,		lowercase					)

                  self.assertListEqual(
                      list(attentions[0].shape[-3:]					) ,		[self.model_tester.num_heads[0], window_size_squared, window_size_squared] ,		)
                  lowerCAmelCase    		=			len(lowercase					)

                  # Check attention is always last and order is fine
                  lowerCAmelCase    		=			True
                  lowerCAmelCase    		=			True
                  lowerCAmelCase    		=			model_class(lowercase					)
                  model.to(lowercase					)
                  model.eval()
                  with torch.no_grad():
                        lowerCAmelCase    		=			model(**self._prepare_for_class(lowercase ,		lowercase					)					)

                  if hasattr(self.model_tester ,		"""num_hidden_states_types"""					):
                        lowerCAmelCase    		=			self.model_tester.num_hidden_states_types
                  else:
                        # also another +1 for reshaped_hidden_states
                        lowerCAmelCase    		=			2
                  self.assertEqual(out_len + added_hidden_states ,		len(lowercase					)					)

                  lowerCAmelCase    		=			outputs.attentions

                  self.assertEqual(len(lowercase					) ,		lowercase					)

                  self.assertListEqual(
                      list(self_attentions[0].shape[-3:]					) ,		[self.model_tester.num_heads[0], window_size_squared, window_size_squared] ,		)





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		lowercase ,		lowercase					)						->	List[str]:
            lowerCAmelCase    		=			model_class(lowercase					)
            model.to(lowercase					)
            model.eval()

            with torch.no_grad():
                  lowerCAmelCase    		=			model(**self._prepare_for_class(lowercase ,		lowercase					)					)

            lowerCAmelCase    		=			outputs.hidden_states

            lowerCAmelCase    		=			getattr(
                self.model_tester ,		"""expected_num_hidden_layers""" ,		len(self.model_tester.depths					) + 1					)
            self.assertEqual(len(lowercase					) ,		lowercase					)

            # Swinv2 has a different seq_length
            lowerCAmelCase    		=			(
                config.patch_size
                if isinstance(config.patch_size ,		collections.abc.Iterable					)
                else (config.patch_size, config.patch_size)
            )

            lowerCAmelCase    		=			(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

            self.assertListEqual(
                list(hidden_states[0].shape[-2:]					) ,		[num_patches, self.model_tester.embed_dim] ,		)

            lowerCAmelCase    		=			outputs.reshaped_hidden_states
            self.assertEqual(len(lowercase					) ,		lowercase					)

            lowerCAmelCase		,						lowerCAmelCase		,						lowerCAmelCase		,						lowerCAmelCase    		=			reshaped_hidden_states[0].shape
            lowerCAmelCase    		=			(
                reshaped_hidden_states[0].view(lowercase ,		lowercase ,		height * width					).permute(0 ,		2 ,		1					)
            )
            self.assertListEqual(
                list(reshaped_hidden_states.shape[-2:]					) ,		[num_patches, self.model_tester.embed_dim] ,		)





      def        _snake_case     (			self					)						->	Dict:
            lowerCAmelCase		,						lowerCAmelCase    		=			self.model_tester.prepare_config_and_inputs_for_common()

            lowerCAmelCase    		=			(
                self.model_tester.image_size
                if isinstance(self.model_tester.image_size ,		collections.abc.Iterable					)
                else (self.model_tester.image_size, self.model_tester.image_size)
            )

            for model_class in self.all_model_classes:
                  lowerCAmelCase    		=			True
                  self.check_hidden_states_output(lowercase ,		lowercase ,		lowercase ,		lowercase					)

                  # check that output_hidden_states also work using config
                  del inputs_dict["output_hidden_states"]
                  lowerCAmelCase    		=			True

                  self.check_hidden_states_output(lowercase ,		lowercase ,		lowercase ,		lowercase					)





      def        _snake_case     (			self					)						->	int:
            lowerCAmelCase		,						lowerCAmelCase    		=			self.model_tester.prepare_config_and_inputs_for_common()
            lowerCAmelCase    		=			3

            lowerCAmelCase    		=			(
                self.model_tester.image_size
                if isinstance(self.model_tester.image_size ,		collections.abc.Iterable					)
                else (self.model_tester.image_size, self.model_tester.image_size)
            )
            lowerCAmelCase    		=			(
                config.patch_size
                if isinstance(config.patch_size ,		collections.abc.Iterable					)
                else (config.patch_size, config.patch_size)
            )

            lowerCAmelCase    		=			image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
            lowerCAmelCase    		=			image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

            for model_class in self.all_model_classes:
                  lowerCAmelCase    		=			True
                  self.check_hidden_states_output(lowercase ,		lowercase ,		lowercase ,		(padded_height, padded_width)					)

                  # check that output_hidden_states also work using config
                  del inputs_dict["output_hidden_states"]
                  lowerCAmelCase    		=			True
                  self.check_hidden_states_output(lowercase ,		lowercase ,		lowercase ,		(padded_height, padded_width)					)





      def        _snake_case     (			self					)						->	Any:
            lowerCAmelCase    		=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_masked_image_modeling(*lowercase					)





      def        _snake_case     (			self					)						->	Tuple:
            lowerCAmelCase    		=			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*lowercase					)





      @slow
      def        _snake_case     (			self					)						->	Any:
            for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  lowerCAmelCase    		=			SwinvaModel.from_pretrained(lowercase					)
                  self.assertIsNotNone(lowercase					)





      def        _snake_case     (			self					)						->	Any:
            lowerCAmelCase		,						lowerCAmelCase    		=			self.model_tester.prepare_config_and_inputs_for_common()

            lowerCAmelCase    		=			_config_zero_init(lowercase					)
            for model_class in self.all_model_classes:
                  lowerCAmelCase    		=			model_class(config=lowercase					)
                  for name, param in model.named_parameters():
                        if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
                              self.assertIn(
                                  ((param.data.mean() * 1e9).round() / 1e9).item() ,		[0.0, 1.0] ,		msg=f'Parameter {name} of model {model_class} seems not properly initialized' ,		)







@require_vision
@require_torch
class 						lowercase   (   unittest.TestCase   ):





      @cached_property
      def        _snake_case     (			self					)						->	Any:
            return (
                AutoImageProcessor.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256"""					)
                if is_vision_available()
                else None
            )





      @slow
      def        _snake_case     (			self					)						->	Tuple:
            lowerCAmelCase    		=			SwinvaForImageClassification.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256"""					).to(
                lowercase					)
            lowerCAmelCase    		=			self.default_image_processor

            lowerCAmelCase    		=			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
            lowerCAmelCase    		=			image_processor(images=lowercase ,		return_tensors="""pt"""					).to(lowercase					)

            # forward pass
            with torch.no_grad():
                  lowerCAmelCase    		=			model(**lowercase					)

            # verify the logits
            lowerCAmelCase    		=			torch.Size((1, 1_000)					)
            self.assertEqual(outputs.logits.shape ,		lowercase					)
            lowerCAmelCase    		=			torch.tensor([-0.3_947, -0.4_306, 0.0_026]					).to(lowercase					)
            self.assertTrue(torch.allclose(outputs.logits[0, :3] ,		lowercase ,		atol=1e-4					)					)






